Browsing by Author "Cazenave, Alexandre Brice"

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    2020 Peanut Variety and Quality Evaluation Results. I. Agronomic and Grade Data
    Balota, Maria; Dunne, Jeffrey; Cazenave, Alexandre Brice; Anco, Dan; Nixon, Wayne (Virginia Cooperative Extension, 2021-02-16)
    Due to suitability to the environmental conditions and existence of a strong peanut industry tailored to process primarily the large-seeded Virginia-type peanut, growers in Virginia, North Carolina, and South Carolina generally grow Virginia-type cultivars. In the view of a common interest in the Virginia-type peanut, the three states are working together through a multi-state project, the Peanut Variety Quality Evaluation (PVQE), to evaluate advanced breeding lines and commercial cultivars throughout their production regions. The objectives of this project are: 1) to determine yield, grade, quality, and disease response of commercial cultivars and advanced breeding lines at various locations in Virginia and the Carolinas, 2) develop a database for Virginia-type peanut to allow research-based selection of the best genotypes by growers, industry, and the breeding programs, and 3) to identify the most-suited peanut genotypes for various regions that can be developed into varieties. This report contains agronomic and grade data of the PVQE tests in 2020.
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    Development of high-throughput phenotyping methods and evaluation of morphological and physiological characteristics of peanut in a sub-humid environment
    Sarkar, Sayantan (Virginia Tech, 2021-01-05)
    Peanut (Arachis hypogaea L.) is an important food crop in the USA and worldwide with high net returns but yield in excess of 4500 kg ha-1 is needed to offset the production costs. Because yield is limited by biotic and abiotic stresses, cultivars with stress tolerance are needed to optimize yield. The U.S. peanut mini-core germplasm collection is a valuable resource that breeders can use to improve stress tolerance in peanut. Phenotyping for plant height, leaf area, and leaf wilting have been used as proxies for the desired tolerance traits. However, proximal data collection, i.e. measurements are taken on individual plants or in the proximity, is slow. Remote data collection and machine learning techniques for analysis offer a high-throughput phenotyping (HTP) alternative to manual measurements that could help breeding for stress tolerance. The objectives of this study were to 1) develop HTP methods using aerial remote sensing; 2) evaluate the mini-core collection in SE Virginia; and 3) perform a detailed physiological analysis on a sub-set of 28 accessions from the mini-core collection under drought stress, i.e. the sub-set was selected based on contrasting differences under drought in three states, Virginia, Texas, and Oklahoma. To address these objectives, replicated experiments were performed in the field at the Tidewater Agricultural Research and Extension Center in Suffolk, VA, in 2017, 2018, and 2019, under rainfed, irrigated, and controlled conditions using rainout shelters to induce drought. Proximal data collection involved physiological, morphological, and yield measurements. Remote data collection was performed aerially and included collection of red-green-blue (RGB) images and canopy reflectance in the visible, near infra-red, and infra-red spectra. This information was used to estimate plant characteristics related to growth and drought tolerance. Under objective 1), we developed HTP for plant height with 85-95% accuracy, LAI with 85-88% accuracy, and wilting with 91-99% accuracy; this was done with significant reduction of time as compared to proximal data collection. Under objectives 2) and 3), we determined that shorter genotypes were more drought tolerant than taller genotypes; and identified CC650 less wilted and with increased carbon assimilation, electron transport, quantum efficiency, and yield than other accessions.
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    Peanut Variety and Quality Evaluation Results, 2018. I, Agronomic and Grade Data
    Balota, Maria; Dunne, Jeffrey; Cazenave, Alexandre Brice; Anco, Dan (Virginia Cooperative Extension, 2019-01-15)
    Provides data about peanut varieties grown at test locations in Virginia and the Carolinas. Data include planting dates, weather conditions, cultural practices, soil type, fertilizers, irrigation, use of herbicides and insecticides, and harvest yield.
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    Peanut Variety and Quality Evaluation Results, 2018. II, Quality Data
    Balota, Maria; Dunne, Jeffrey; Cazenave, Alexandre Brice; Anco, Dan (Virginia Cooperative Extension, 2019-03-07)
    Presents data on Virginia-type peanut cultivars and on new breeding lines of these cultivars. Compares quality of kernels and pods of peanut varieties grown at test plots.
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    Peanut variety and quality evaluation results, 2019. II Quality data
    Dunne, Jeffrey; Cazenave, Alexandre Brice; Anco, Dan (Virginia Cooperative Extension, 2020/03)
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    Peanut Variety and Quality Evaluation Results, 2020. II, Quality Data
    Balota, Maria; Dunne, Jeffrey C.; Cazenave, Alexandre Brice; Anco, Daniel J.; Nixon, Wayne (Virginia Cooperative Extension, 2021-03)
    Presents data on Virginia-type peanut cultivars and on new breeding lines of these cultivars. Compares quality of kernels and pods of peanut varieties grown at test plots.
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    RGB-image method enables indirect selection for leaf spot resistance and yield estimation in a groundnut breeding program in Western Africa
    Sie, Emmanuel Kofi; Oteng-Frimpong, Richard; Kassim, Yussif Baba; Puozaa, Doris Kanvenaa; Adjebeng-Danquah, Joseph; Masawudu, Abdul Rasheed; Ofori, Kwadwo; Danquah, Agyemang; Cazenave, Alexandre Brice; Hoisington, David; Rhoads, James; Balota, Maria (Frontiers, 2022-08-04)
    Early Leaf Spot (ELS) caused by the fungus Passalora arachidicola and Late Leaf Spot (LLS) also caused by the fungus Nothopassalora personata, are the two major groundnut (Arachis hypogaea L.) destructive diseases in Ghana. Accurate phenotyping and genotyping to develop groundnut genotypes resistant to Leaf Spot Diseases (LSD) and to increase groundnut production is critically important in Western Africa. Two experiments were conducted at the Council for Scientific and Industrial Research-Savanna Agricultural Research Institute located in Nyankpala, Ghana to explore the effectiveness of using RGB-image method as a high-throughput phenotyping tool to assess groundnut LSD and to estimate yield components. Replicated plots arranged in a rectangular alpha lattice design were conducted during the 2020 growing season using a set of 60 genotypes as the training population and 192 genotypes for validation. Indirect selection models were developed using Red-Green-Blue (RGB) color space indices. Data was collected on conventional LSD ratings, RGB imaging, pod weight per plant and number of pods per plant. Data was analyzed using a mixed linear model with R statistical software version 4.0.2. The results showed differences among the genotypes for the traits evaluated. The RGB-image method traits exhibited comparable or better broad sense heritability to the conventionally measured traits. Significant correlation existed between the RGB-image method traits and the conventionally measured traits. Genotypes 73-33, Gha-GAF 1723, Zam-ICGV-SM 07599, and Oug-ICGV 90099 were among the most resistant genotypes to ELS and LLS, and they represent suitable sources of resistance to LSD for the groundnut breeding programs in Western Africa.